Structural damage identification has been the focus of engineering fields, while the existing damage identification methods heavily depend on extracted “hand-crafted” features. Recently, due to the powerful feature learning capability of deep learning, it has been widely used in structural damage identification. However, those methods only consider the local dependence or temporal relation of data. Thus, in this paper, a structural damage identification method by combining the convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed. The CNN model is used to extract the local dependence of data, and the GRU model is used to extract the temporal feature of data. These two extracted feature matrices are spliced horizontally to a fused eigenvector. The eigenvector is input to the final softmax classifier layer to identify the structural damage state. Experiments on a scale model of the three-span continuous rigid frame bridge shown that the CNN-GRU model performs significantly better than CNN, LSTM, and GRU models for structural damage identification.

For the purpose of solving the high quality required in some image based measurement systems, a new calibration algorithm for a non-uniform illumination field image is proposed on the basis of some existing algorithm. In this algorithm, by analysing the characteristics of the potential function for target detection, a new potential function for target detection was designed. A function for standardisation of variety was deduced thereafter. After that, the mapping function from the original image to the background calibration image was given. Numerical simulations showed that the proposed algorithm can restrain the non-uniform background illumination field and calibrate the strong fluctuant background of the illumination field image and thus improve the quality of the image. Meanwhile, the algorithm is simple to carry out and very suitable in practice.